Please fill out the following survey:
dertristan.limesurvey.net/qmir-2026-welcome
Figure 1: Your answers to: What is your primary field of study (major/Hauptfach)?
Figure 2: Your answers to: What is your secondary field of study (minor/Nebenfach)?
Figure 3: Your answers to: Which semester of your Bachelor’s program are you currently in?
Figure 4: Your answers to: Have you taken any quantitative methods or statistics course before?
Figure 5: Your answers to: Why did you register for this course specifically?
Figure 6: Your answers to: Which statements best describes how you feel about quantitative methods right now?
# Create a named vector for renaming
tool_names <- c(
"SQ001." = "R",
"SQ002." = "RStudio",
"SQ004." = "Rmarkdown",
"SQ005." = "Git",
"SQ006." = "GitHub",
"SQ007." = "Python",
"SQ008." = "Stata",
"SQ009." = "SPSS",
"SQ010." = "MS Excel",
"SQ011." = "LateX",
"SQ012." = "Command line/terminal",
"SQ013." = "Positron",
"SQ014." = "Markdown",
"SQ015." = "Quarto",
"SQ016." = "Overleaf",
"SQ017." = "MS Word",
"SQ018." = "VS Code"
)
tools_heardof_long <- responses |>
select(contains("Q509412")) |>
# rename columns by replacing SQ### with tool names
rename_with(~ str_replace_all(., tool_names), .cols = everything()) |>
# pivot longer for ggplot
pivot_longer(
cols = everything(),
names_to = "tool",
values_to = "answer"
) |>
mutate(tool = str_replace(tool, "Q509412.", ""))
max_count <- tools_heardof_long |>
group_by(tool) |>
count(answer, sort = T) |>
ungroup() |>
slice(1) |>
pull(n)
ggplot(
data = tools_heardof_long,
mapping = aes(y = fct_infreq(answer))
) +
geom_bar(
fill = "white",
color = color_highlight,
width = 0.5
) +
theme_pubr() +
labs(
x = "Count",
y = "Answer"
) +
theme(aspect.ratio = 0.618) +
scale_x_continuous(
breaks = seq(from = 0, to = max_count, by = 1)
) +
facet_wrap(~tool)Figure 7: Your answers to: Which of the following tools have you heard of before?
tools_used_long <- responses |>
select(contains("Q928658")) |>
# rename columns by replacing SQ### with tool names
rename_with(~ str_replace_all(., tool_names), .cols = everything()) |>
# pivot longer for ggplot
pivot_longer(
cols = everything(),
names_to = "tool",
values_to = "answer"
) |>
mutate(tool = str_replace(tool, "Q928658.", ""))
max_count <- tools_used_long |>
group_by(tool) |>
count(answer, sort = T) |>
ungroup() |>
slice(1) |>
pull(n)
ggplot(
data = tools_used_long,
mapping = aes(y = fct_infreq(answer))
) +
geom_bar(
fill = "white",
color = color_highlight,
width = 0.5
) +
theme_pubr() +
labs(
x = "Count",
y = "Answer"
) +
theme(aspect.ratio = 0.618) +
scale_x_continuous(
breaks = seq(from = 0, to = max_count, by = 1)
) +
facet_wrap(~tool)Figure 8: Your answers to: Which of the following tools have you actively worked with before?
Figure 9: Your answers to: How would you describe your overall confidence in working with code?
items_map <- c(
"Q751740" = "Statistical analysis is mainly about determining whether a hypothesis is true or false.",
"Q91718" = "Empirical data can increase or decrease our confidence in a theoretical claim.",
"Q797817" = "Uncertainty should be explicitly reported when presenting empirical results.",
"Q235999" = "Scientific knowledge is always provisional and subject to revision with new data.",
"Q402631" = "A single statistical results can definitely prove or disprove a theory.",
"Q613049" = "Models are simplifications of reality rather than exact representations."
)
responses |>
select(all_of(c(
"Q751740",
"Q91718",
"Q797817",
"Q235999",
"Q402631",
"Q613049"
))) |>
pivot_longer(
cols = everything(),
names_to = "question",
values_to = "response"
) |>
mutate(
question = items_map[question],
question = str_wrap(question, width = 40)
) |>
ggplot(
mapping = aes(x = response)
) +
geom_bar(
fill = "white",
color = color_highlight,
width = 0.5
) +
theme_pubr() +
labs(
y = "Count",
x = "Answer"
) +
scale_x_continuous(
breaks = 1:5,
labels = c(
"Strongly\ndisagree",
"disagree",
"neither\nagree nor\ndisagree",
"agree",
"strongly\nagree"
)
) +
theme(
aspect.ratio = 0.618,
axis.text.x = element_text(size = 8)
) +
facet_wrap(~question)Figure 10: Your answers in the agreement/disagreement items I.
items_map <- c(
"Q859946" = "Whether it is statistically significant.",
"Q65120" = "The size of the effect.",
"Q157010" = "The uncertainty around the estimate.",
"Q233596" = "Whether the result fits the theory.",
"Q699812" = "Whether the model assumptions seem plausible.",
"Q975313" = "I am usually unsure how to interpret such results."
)
responses |>
select(all_of(names(items_map))) |>
pivot_longer(
cols = everything(),
names_to = "question",
values_to = "response"
) |>
mutate(
question = items_map[question],
question = str_wrap(question, width = 40)
) |>
ggplot(
mapping = aes(x = response)
) +
geom_bar(
fill = "white",
color = color_highlight,
width = 0.5
) +
theme_pubr() +
labs(
y = "Count",
x = "Answer"
) +
scale_x_continuous(
breaks = 1:5,
labels = c(
"Strongly\ndisagree",
"disagree",
"neither\nagree nor\ndisagree",
"agree",
"strongly\nagree"
)
) +
theme(
aspect.ratio = 0.618,
axis.text.x = element_text(size = 8)
) +
facet_wrap(~question)Figure 11: Your answers in the agreement/disagreement items II: Imagine you read a study claiming that attending a political debate doubles the likelihood of a person voting in the next election. When you see a numerical result like this (e.g., an estimate or coefficient), what are you usually most interested in?
Please make sure to install all software and send me your GitHub name or email address.
QMIR – February 9, 2026 – Week 1